Projects / Programmes
A model for on-line selection of roughing parameters of the EDM process
Code |
Science |
Field |
Subfield |
2.10.00 |
Engineering sciences and technologies |
Manufacturing technologies and systems |
|
Code |
Science |
Field |
T130 |
Technological sciences |
Production technology |
EDM, electrical discharge machining, inductive machine learning, non-parametric modelling, rough machining, process monitoring
Researchers (7)
no. |
Code |
Name and surname |
Research area |
Role |
Period |
No. of publicationsNo. of publications |
1. |
09006 |
PhD Mihael Junkar |
Manufacturing technologies and systems |
Head |
2004 - 2006 |
552 |
2. |
24419 |
Boštjan Juriševič |
Manufacturing technologies and systems |
Researcher |
2004 - 2006 |
62 |
3. |
17076 |
PhD Davorin Kramar |
Manufacturing technologies and systems |
Researcher |
2004 - 2006 |
447 |
4. |
12260 |
PhD Andrej Lebar |
Manufacturing technologies and systems |
Researcher |
2004 - 2006 |
309 |
5. |
23469 |
PhD Henri Orbanić |
Manufacturing technologies and systems |
Junior researcher |
2004 - 2006 |
166 |
6. |
18000 |
Bruno Stropnik |
|
Technical associate |
2004 - 2005 |
1 |
7. |
18553 |
PhD Joško Valentinčič |
Manufacturing technologies and systems |
Researcher |
2004 - 2006 |
444 |
Organisations (1)
Abstract
The sinking electrical discharge machining process (EDM) is performed in the gap between the electrode and the workpiece. The gap is filled with dielectric and the size of the gap is controlled by the servo system of the EDM machine. Electric pulses produced by electric generator cause a breakdown of the dielecric in the gap. After the breakdown, the discharge occurs, which causes a material removal in the place where it occurs. The material removal rate depends on the machining parameters (discharge current, discharge duration etc.), which are grouped into machining regimes. To achieve the highest material removal rate on the given machining surface, the roughing regime has to be selected according to the eroding surface size, which is defined as the projection of the machining surface to the plane perpendicular to the direction of the electrode movement. Most often, the eroding surface size varies during the machining. Thus, the roughing regime has to be selected on-line. Since the material removal rate of the EDM process is relatively small, the on-line selection of the roughing regime is essential for fast and cheap production.
The system for on-line selection of the roughing regime will be based on the acquisition of the voltage signal in the gap. The attributes on the voltage signal will be attained by inductive machine learning methods and their values will be calculated on-line by the analysator. Non-parametric model, which will on-line select the appropriate roughing regime, will be build by the conditional average estimator method. The model will perform the on-line selection of the roughing regime and thus the highest material removal rate on the given machining surface will be obtained.